In Regression, a Friendly Guide you will learn how to
Pick the right regression model
Forecast sales, customer acquisition, or customer influences using linear regression
Analyze customer churn and marketing strategies using logistic regression
Model monthly subscriptions or identify profitable startups by sector using count regression
Interpret the assumptions, structure, and assessment of regression models
Effectively select variables and specify models
Simulate outcomes and resample data for modeling and assessment
Regression, a Friendly Guide teaches you how to predict future outcomes and understand important relationships in your data using regression. This easy-to-read book is written for readers who don't have an advanced math background. It introduces essential concepts and notation step-by-step, with plenty of entertaining and concrete examples. You'll progress from the basics to advanced models that are perfect for handling messy or unstructured data. As you absorb the lucid explanations and complete the exercises, you'll build a real intuition for regression. You'll understand how your models work and develop the tools and vocabulary to explain why their predictions are accurate.
about the technology
Regression models are tools for exploring relationships in data. They see heavy use in data science for making predictions, measuring growth, and analyzing probabilities. A proper understanding of how regression models work will ensure you can make and defend sound data-driven decisions.
about the book
Regression, a Friendly Guide teaches you to build, assess, and interpret regression models. In each chapter, new modelling paradigms are introduced with simple language and illustrative examples. You'll steadily build up your theoretical understanding until you can intuitively interpret abstract regression models and their underlying mathematics.
You won't need any graduate math knowledge to get started, as you explore relevant real-world examples taken from politics, building science, medicine, and other industries. Start off by building linear and logistic regression models with one or more variables, and master regression for count models. Learn to use simulation methods to assess your models, and discover advanced techniques like generalized additive models, penalty methods, and quantile regression. You'll even learn how to interpret your models and findings for non-technical coworkers, to ensure your whole organization is making reliable decisions driven by accurate analysis.